TrainDimensionalityReduction - Train Dimensionality Reduction¶

Train a dimensionality reduction model

Detailed description¶

Trainer for dimensionality reduction algorithms (autoencoders, PCA, SOM). All input samples are used to compute the model, like other machine learning models. The model can be used in the ImageDimensionalityReduction and VectorDimensionalityReduction applications.

Parameters¶

This section describes in details the parameters available for this application. Table [1] presents a summary of these parameters and the parameters keys to be used in command-line and programming languages. Application key is TrainDimensionalityReduction .

[1]	Table: Parameters table for Train Dimensionality Reduction.

Parameter Key	Parameter Name	Parameter Type
io	Input and output data	Group
io.vd	Input Vector Data	Input vector data
io.out	Output model	Output File name
io.stats	Input XML image statistics file	Input File name
feat	Field names to be used for training.	String list
algorithm	algorithm to use for the training	Choices
algorithm som	OTB SOM	Choice
algorithm autoencoder	Shark Autoencoder	Choice
algorithm pca	Shark PCA	Choice
algorithm.som.s	Map size	String list
algorithm.som.n	Neighborhood sizes	String list
algorithm.som.ni	NumberIteration	Int
algorithm.som.bi	BetaInit	Float
algorithm.som.bf	BetaFinal	Float
algorithm.som.iv	InitialValue	Float
algorithm.autoencoder.nbiter	Maximum number of iterations during training	Int
algorithm.autoencoder.nbiterfinetuning	Maximum number of iterations during training	Int
algorithm.autoencoder.epsilon	Epsilon	Float
algorithm.autoencoder.initfactor	Weight initialization factor	Float
algorithm.autoencoder.nbneuron	Size	String list
algorithm.autoencoder.regularization	Strength of the regularization	String list
algorithm.autoencoder.noise	Strength of the noise	String list
algorithm.autoencoder.rho	Sparsity parameter	String list
algorithm.autoencoder.beta	Sparsity regularization strength	String list
algorithm.autoencoder.learningcurve	Learning curve	Output File name
algorithm.pca.dim	Dimension of the output of the pca transformation	Int
ram	Available RAM (Mb)	Int
inxml	Load otb application from xml file	XML input parameters file
outxml	Save otb application to xml file	XML output parameters file

[Input and output data]: This group of parameters allows setting input and output data.

Input Vector Data: Input geometries used for training (note : all geometries from the layer will be used).
Output model: Output file containing the estimated model (.txt format).
Input XML image statistics file: XML file containing mean and variance of each feature.

Field names to be used for training.: List of field names in the input vector data used as features for training.

algorithm to use for the training: Choice of the dimensionality reduction algorithm to use for the training. Available choices are:

OTB SOM: This group of parameters allows setting SOM parameters. .

Map size: Sizes of the SOM map (one per dimension). For instance, [12;15] means a 2D map of size 12x15. Support2D to 5D maps.

Neighborhood sizes: Sizes of the initial neighborhood in the SOM map (one per dimension). The number of sizes should be the same as the map sizes.

NumberIteration: Number of iterations for SOM learning.

BetaInit: Initial learning coefficient.

BetaFinal: Final learning coefficient.

InitialValue: Maximum initial neuron weight.

Shark Autoencoder: This group of parameters allows setting Shark autoencoder parameters. .

Maximum number of iterations during training: The maximum number of iterations used during training.

Maximum number of iterations during training: The maximum number of iterations used during fine tuning of the whole network.

Epsilon: Epsilon.

Weight initialization factor: Parameter that control the weight initialization of the autoencoder.

Size: The number of neurons in each hidden layer.

Strength of the regularization: Strength of the L2 regularization used during training.

Strength of the noise: Strength of the noise.

Sparsity parameter: Sparsity parameter.

Sparsity regularization strength: Sparsity regularization strength.

Learning curve: Learning error values.

Shark PCA: This group of parameters allows setting Shark PCA parameters. .

Dimension of the output of the pca transformation: Dimension of the output of the pca transformation.

Available RAM (Mb): Available memory for processing (in MB).

Load otb application from xml file: Load otb application from xml file.

Save otb application to xml file: Save otb application to xml file.

Example¶

To run this example in command-line, use the following:

otbcli_TrainDimensionalityReduction -io.vd cuprite_samples.sqlite -io.out mode.ae -algorithm pca -algorithm.pca.dim 8 -feat value_0 value_1 value_2 value_3 value_4 value_5 value_6 value_7 value_8 value_9

To run this example from Python, use the following code snippet:

#!/usr/bin/python

# Import the otb applications package
import otbApplication

# The following line creates an instance of the TrainDimensionalityReduction application
TrainDimensionalityReduction = otbApplication.Registry.CreateApplication("TrainDimensionalityReduction")

# The following lines set all the application parameters:
TrainDimensionalityReduction.SetParameterString("io.vd", "cuprite_samples.sqlite")

TrainDimensionalityReduction.SetParameterString("io.out", "mode.ae")

TrainDimensionalityReduction.SetParameterString("algorithm","pca")

TrainDimensionalityReduction.SetParameterInt("algorithm.pca.dim", 8)

TrainDimensionalityReduction.SetParameterStringList("feat", ['value_0', 'value_1', 'value_2', 'value_3', 'value_4', 'value_5', 'value_6', 'value_7', 'value_8', 'value_9'])

# The following line execute the application
TrainDimensionalityReduction.ExecuteAndWriteOutput()

Limitations¶

None

Authors¶

This application has been written by OTB-Team.